Building trust in AI from the ground up: How you can secure the data behind it

AI raises the stakes

As AI adoption accelerates, is your organization keeping pace with the data risks? These risks are not just new challenges — they can exacerbate existing weaknesses in data security and governance. AI adoption magnifies these issues, bringing the underlying issues to the surface and causing organizations to question the overall integrity of the AI model due to the lack of data trust.

As AI-powered tools become more embedded in daily operations, security, data and information leaders now face two important priorities:

1. Training AI models on high-quality data. Without proper governance, AI models risk reinforcing biases, misinterpreting information or delivering unreliable outputs.
2. Applying governance and security controls to AI-powered workflows. As AI interacts with sensitive business data, organizations should enforce policies that can prevent compliance failures, security breaches and unauthorized access.

The rising urgency of data protection and trust

Data protection and trust have become a top priority for business and technology leaders. In PwC’s 2025 PwC Digital Trust Insights Survey, executives were asked to rank a list of priorities based on areas specific to their roles. Data protection and trust was ranked:

• #1 priority of business executives, with 48% ranking data protection and trust as their top cyber investment priority, ahead of tech modernization (43%).

• #2 top priority of tech executives, with 28% prioritizing data protection and trust as their second priority behind cloud security (34%).

Yet, many organizations still struggle with their data estate, often making it difficult to enforce governance and security measures.

Data risks that could undermine your AI

How much can you trust the data powering your AI? If data is inaccurate, unprotected or exposed to security gaps, AI models can generate misleading insights, introduce compliance risks and compromise sensitive information. Before organizations can unlock AI’s potential, they should first mitigate key data risks, including:

• Data quality risks: AI models trained on unstructured, redundant or outdated content can generate unreliable outputs, leading to poor decision-making.
• Data protection risks: Uncontrolled access permissions to sensitive data coupled with AI tools can expose sensitive data to unauthorized users, increasing security vulnerabilities.
• Data compliance risks: Without proper classification and oversight, AI-driven automation may process sensitive data in ways that violate privacy regulations.
• Data exposure risks: AI tools with insufficient access controls can increase the risk of insider threats, unauthorized data sharing and leakage of sensitive information.

Without addressing these foundational risks, AI-driven tools may introduce more uncertainty than innovation. Many organizations often need a clearer strategy for governing the data that fuels AI — or they risk AI working against them instead of for them.

Turning risk into resilience

How can organizations embrace AI and manage data risk? Technology plays an integral role in mitigating risks and strengthening AI readiness. The proper tools can help organizations improve data visibility, enforce security policies and maintain compliance without slowing down progress. A strong data governance and security framework enables AI models to operate on more precise, trusted data while helping reduce exposure to breaches and regulatory failures.

Microsoft’s data security and governance solutions are designed to address these challenges head-on, helping organizations protect their data while enabling AI to function effectively. Microsoft’s capabilities are designed to help reduce key risks organizations can face:

• Data quality – Microsoft Purview (Purview) enables organizations to classify, label and manage data across their environment, helping AI models work with precise and trusted data. Sensitivity labeling capabilities help control which data Copilot can access.

• Data protection – Microsoft’s security solutions, including Purview’s data security module, help safeguard sensitive information through encryption, policy enforcement and identity management.

• Data compliance – Built-in compliance capabilities within Purview help organizations align AI-driven processes with evolving regulatory requirements.

• Data exposure – Insider Risk Management in Purview helps detect and prevent unauthorized access, reducing the risk of insider threats and shadow IT. Labeling and data loss prevention controls provide additional safeguards to help reduce insider threats.

By addressing these risks before scaling AI adoption, organizations can deploy AI models that drive growth while maintaining security and trust.

Strengthening AI governance

One of the biggest challenges organizations face when adopting AI is content sprawl — unstructured, redundant and outdated information scattered across platforms. Without clear oversight, AI models like Microsoft Copilot may surface or process outdated, irrelevant or even sensitive data, often leading to security risks and unreliable outputs.

Microsoft SharePoint Advanced Management helps organizations gain better control over their data estate, reducing risk and improving AI-driven decision-making. Key capabilities that support AI governance include:

• Managing access sprawl – Strengthens site ownership policies and permissions, so only the desired users have access to sensitive content.
• Reducing redundant or outdated content – Identifies inactive sites and helps enforce governance policies to keep AI models from pulling obsolete or unnecessary data.
• Monitoring content usage – Provides visibility into how data is accessed and modified, helping organizations track patterns that could indicate security risks.

By integrating SharePoint Advanced Management into their governance strategy, organizations can reduce data exposure, enhance AI model precision, and maintain compliance while enabling more effective and secure AI adoption.

An expanded security ecosystem

Beyond governance, Microsoft’s broader security ecosystem helps organizations mitigate data risks and protect AI-driven workflows. Microsoft Sentinel (Sentinel) provides centralized reporting and real-time threat detection, while Microsoft Entra (Entra) enforces secure access controls. Security Copilot accelerates incident response by using AI to analyze threats and automate risk mitigation. Together, these tools strengthen data protection, improve compliance readiness and help reduce AI-related vulnerabilities.

Strengthen AI data security and governance with Microsoft

As AI becomes more embedded in business processes, organizations should address data security, governance and compliance challenges to help prevent risks from escalating. Taking the following steps can help build a strong foundation for AI-driven innovation while decreasing exposure to threats and regulatory concerns:

• Improve compliance and governance controls for AI workflows – Use Purview to classify, label and protect sensitive data, so AI models interact with governed and compliant information.
• Reduce data exposure and access sprawl – Implement Entra to enforce least-privilege access and SharePoint Advanced Management to monitor content usage and secure AI-accessible data.
• Establish a trusted data foundation for AI models – Leverage Purview to map, inventory and clean up unstructured data, so AI models are trained on precise and authoritative sources.
• Enhance AI security and threat response – Utilize Sentinel for centralized monitoring and Security Copilot to accelerate detection and mitigation of AI-related security risks.
• Adopt a phased approach to AI governance – Start with targeted governance policies, train teams on Responsible AI use and refine security controls before scaling AI adoption.

By proactively addressing these areas, organizations can confidently deploy AI while maintaining strong security, governance and compliance controls.